Biomarkers of rapid progression in advanced non-small cell lung cancer

Methods and kits for identifying rapidly progressing lung cancer in a subject are provided. The method includes obtaining a biological sample from the subject and assaying a level of a biomarker in a biomarker panel in the biological sample where the panel includes at least one biomarker selected from Table I or Table II. The method further includes determining with the subject is treatment naive or has received at least one treatment; and comparing the level of the biomarker in the subject's sample to a cutoff value listed in Table I for treatment naive subjects or Table II for previously treated subjects. The method further includes determining whether the subject's level is above or below the cutoff value to determine whether the subject has rapidly progressing lung cancer.

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Description
RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 371 of International Application No. PCT/US2014/069009, filed Dec. 8, 2014, which claims the benefit of U.S. Provisional Application No. 61/913,740, filed Dec. 9, 2013, which are incorporated by reference herein in their entirety.

TECHNICAL FIELD

The present invention relates to methods and kits for identifying patients with rapidly progressing disease, and in particular to methods and kits for identifying patients with rapidly progressing non-small cell lung cancer and for determining optimal treatment plans for patients with rapidly progressing disease and for monitoring treatments.

BACKGROUND

Lung cancer is leading cause of cancer-related mortality worldwide, with a projected 159,480 patients succumbing to the disease in the US in 2014.(1) Lung cancer is typically characterized as being quite aggressive with poor clinical outcomes that stem from the very rapid proliferation rates, high metastatic potential, and general insensitivity to available treatment strategies. Non-small cell lung cancer (NSCLC) presents unique challenges to health care providers because of its common late stage of presentation and the poor median overall survival of advanced disease.(2, 3) Patients often become too ill to receive second line treatment as noted by a recent phase III clinical trial where only 37% of the patients randomized to docetaxel at disease progression received treatment.(4)

An objective of this study was reveal circulating biomarkers to identify patients with rapidly progressing NSCLC. This study examined 76 biomarkers that are surrogates for several pathophysiological processes associated with aggressive disease in both frontline (chemo naïve) and second-line and greater (pretreated) patients. A total of 186 patient serum specimens were evaluated. Processes evaluated include angiogenesis, phenotypic transdifferentiation (i.e. EMT, cancer stem cells), cancer cachexia, chronic inflammation, and immune system response.

Identification of patients with rapidly-progressing disease who are insensitive to standard platinum double-based chemotherapy will provide clinical implications.

There is a need in the art for screening methods and kits that identify patients with rapidly progressing disease in patients that are treatment naïve and in patients that have received a treatment.

BRIEF SUMMARY

Methods and kits for identifying rapidly progressing lung cancer in a subject are provided. The method includes obtaining a biological sample from the subject and assaying a level of a biomarker in a biomarker panel in the biological sample where the panel includes at least one biomarker selected from Table I or Table II. The method is dependent on whether the subject is treatment naïve or has received at least one treatment; and comparing the level of the biomarker in the subject's sample to a cutoff value listed in Table I for treatment naïve subjects or Table II for previously treated subjects. The method further includes determining whether the subject's level is above or below the cutoff value to determine whether the subject has rapidly progressing lung cancer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates representative ‘Box and Whisker’ plots indicating distribution of biomarker levels in the frontline chemotherapy cohort, separated based on a 90 day cutoff for rapid disease progression. Shown are TNF-α (panel A), sTNFRII (panel B), IL-6 (panel C), sVEGFR3 (panel D), betacellulin (Panel E) and Total PSA (panel F). All biomarker levels are provided in pg/mL.

FIG. 2 illustrates representative ‘Box and Whisker’ plots indicating distribution of biomarker levels in the treated chemotherapy cohort, separated based on a 45 day cutoff for rapid disease progression. Shown are sEGFR (panel A), sTNFRI (panel B), TRAIL (panel C), IGFBP-1 (panel D), IGFBP-2 (Panel E) and HGF (panel F). All biomarker levels are provided in pg/mL.

 DETAILED DESCRIPTION

The present invention will utilize at least one biomarker measured in a biological sample obtained from a subject to identify rapidly progressing lung cancer, and in some embodiments in subjects having rapidly progressing NSCLC. In some embodiments, the at least one biomarker may be selected from a panel of biomarkers. In some embodiments, one or more biomarkers from a panel of biomarkers are used to identify subjects having rapidly progressing NSCLC in subjects that are treatment naïve or that have been previously treated.

The term “biomarker” as used herein, refers to any biological compound that can be measured as an indicator of the physiological status of a biological system. A biomarker may comprise an amino acid sequence, a nucleic acid sequence and fragments thereof. Exemplary biomarkers include, but are not limited to cytokines, chemokines, growth and angiogenic factors, metastasis related molecules, cancer antigens, apoptosis related proteins, proteases, adhesion molecules, cell signaling molecules and hormones.

“Measuring” or “measurement” means assessing the presence, absence, quantity or amount (which can be an effective amount) of a given substance within a sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters. Alternatively, the term “detecting” or “detection” may be used and is understood to cover all measuring or measurement as described herein.

The terms “sample” or “biological sample” as used herein, refers to a sample of biological fluid, tissue, or cells, in a healthy and/or pathological state obtained from a subject. Such samples include, but are not limited to, blood, bronchial lavage fluid, sputum, saliva, urine, amniotic fluid, lymph fluid, tissue or fine needle biopsy samples, peritoneal fluid, cerebrospinal fluid, and includes supernatant from cell lysates, lysed cells, cellular extracts, and nuclear extracts. In some embodiments, the whole blood sample is further processed into serum or plasma samples. In some embodiments, the sample includes blood spotting tests.

The term “subject” or “patient” as used herein, refers to a mammal, preferably a human.

The term “rapid progression” or “rapidly progressing” as used herein, refers to cases of disease that were observed to not respond to chemotherapy or targeted agents and advance (evidence of nascent metastases, increasing tumor volume, etc.) within a defined time interval. Thresholds for rapid progression were set to 90 days after the first treatment for the treatment naïve patients and 45 days after the second or subsequent treatment for the previously treated patients. Circulating levels of 27 biomarkers were found to be significantly associated (p≤0.05) with progression within 90 days of treatment initiation in treatment naive patients. Circulating levels of 34 biomarkers were found to be significantly associated (p≤0.05) with progression within 45 days of treatment initiation in previously treated patients.

Biomarkers

Biomarkers that may be used include but are not limited to cytokines, chemokines, growth and angiogenic factors, metastasis related molecules, cancer antigens, apoptosis related proteins, proteases, adhesion molecules, cell signaling molecules and hormones. In some embodiments, the biomarkers may be proteins that are circulating in the subject that may be detected from a fluid sample obtained from the subject. In some embodiments, the fluid sample may be serum or plasma. In some embodiments, one or more biomarkers from a panel of biomarkers may be used.

In some embodiments, one or more biomarkers may be measured in a biomarker panel. The biomarker panel may include a plurality of biomarkers. In some embodiments, the biomarker panel may include ten or fewer biomarkers. In yet other embodiments, the biomarker panel may include 1, 2, 3, 4, 5, 6 or 7 biomarkers. In some embodiments, the biomarker panel may be optimized from a candidate pool of biomarkers. By way of non-limiting example, the biomarker or biomarker panel may be configured for determining whether a treatment naïve subject is likely to have rapidly progressing disease. In some embodiments, the biomarker or biomarker panel may be configured for determining whether a previously treated subject is likely to have rapidly progressing disease.

In some embodiments, the biomarker panel may include biomarkers from several biological pathways. By way of non-limiting example, the biomarkers may be associated the tumor necrosis factor (TNF) family, the epidermal growth factor (EGF) family, the vascular endothelial growth factor (VEGF) family, the Insulin-like growth factor (IGF) family and/or associated with angiogenesis. In some embodiments, the TNF family may include, but is not limited to TNF-RI, TNF-RII, TNF-α and TRAIL. In some embodiments, the EGF family may include but is not limited to betacellulin, amphiregulin, and soluble-EGFR. In some embodiments, the VEGF family may include but is not limited to VEGF-A, VEGF-C, and soluble-VEGFR3. In some embodiments, the IGF family may include but is not limited to IGF-I, IGF-II, IGFBPs-2,-3, and -7. In some embodiments, the biomarkers associated with angiogenesis may include follistatin, IL-6, endoglin, PDGF-BB, IGF-1, and endothelin-1, PLGF, IL-8, MMP-2, HGF, sVEGFR2, VEGF-A, leptin, PDGF-AA, and others. In some embodiments, the biomarker panel may include one or more biomarkers from a panel of biomarkers. In some embodiments, the one or more biomarkers may be selected from the list of biomarkers in Table I. In some embodiments, the one or more biomarkers may be selected from the list of biomarkers in Table II. In some embodiments, other biomarkers may be used and may be combined with the biomarkers listed in Tables I and II.

In some embodiments, patients with rapid disease progression in a treatment naïve group may be identified using one or more biomarkers selected from a panel of biomarkers listed Table I. In some embodiments, the one or more biomarkers may be selected from the group of biomarkers identified in Table I as having a p-value of 0.01 or less. In some embodiments, the one or more biomarkers may include at least one biomarker from Table I having a p-value of 0.01 or less and at least one biomarker from Table I having a p-value of 0.05 or less. In some embodiments, the one or more biomarkers may include biomarkers selected from the group consisting of sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6. In some embodiments, the biomarkers may include 3, 4, 5, 6 or 7 biomarkers selected from the group consisting of sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6 and may also include additional biomarkers. In some embodiments, patients with rapid disease progression in a treatment naïve group may be identified using a panel of one or more biomarkers selected from Table I where the panel may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 biomarkers.

In some embodiments, patients with rapid disease progression in a previously treated group may be identified using one or more biomarkers selected from a panel of biomarkers selected from Table II. In some embodiments, the one or more biomarkers may be selected from the group of biomarkers identified in Table II as having a p-value of 0.01 or less. In some embodiments, the one or more biomarkers may include at least one biomarker from Table II having a p-value of 0.01 or less and at least one biomarker form Table II having a p-value of 0.05 or less. In some embodiments, the one or more biomarkers may be selected from the group consisting of TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, a-fetoprotein, Osteopontin, sVEGFR2 and IL-6. In some embodiments, the one or more biomarkers may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13 biomarkers selected from the group consisting of TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, a-fetoprotein, Osteopontin, sVEGFR2 and IL-6 and may also include additional biomarkers. In some embodiments, patients with rapid disease progression in a previously treated group may be identified using one or more biomarkers selected from Table II where the panel may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 or 34 biomarkers.

In some embodiments, the biomarker panel may be selected using a reference profile that can be made in conjunction with a statistical algorithm used with a computer to implement the statistical algorithm to sort the subject into a group. In some embodiments, the statistical algorithm is a learning statistical classifier system. The learning statistical classifier system can be selected from the following list of non-limiting examples, including Random Forest (RF), Classification and Regression Tree (CART), boosted tree, neural network (NN), support vector machine (SVM), general chi-squared automatic interaction detector model, interactive tree, multiadaptive regression spline, machine learning classifier, and combinations thereof. By way of non-limiting example, exemplary tools for selecting a biomarker panel may be found in WO 2012/054732 and U.S. Provisional Application No. 61/792,710 which are incorporated by reference herein.

Biomarker Measurement

Measurement of a biomarker generally relates to a quantitative measurement of an expression product, which is typically a protein or polypeptide. In some embodiments, the measurement of a biomarker may relate to a quantitative or qualitative measurement of nucleic acids, such as DNA or RNA. The measurement of the biomarker of the subject detects expression levels of one or more biomarkers in subjects having lung cancer and in some embodiments, compares the expression level of each biomarker measured to a cutoff value listed in Table I or in Table II.

Expression of the biomarkers may be measured using any method known to one skilled in the art. Methods for measuring protein expression include, but are not limited to Western blot, immunoprecipitation, immunohistochemistry, Enzyme-linked immunosorbent assay (ELISA), Radio Immuno Assay (RIA), radioreceptor assay, proteomics methods, mass-spectrometry based detection (SRM or MRM) or quantitative immunostaining methods. Methods for measuring nucleic acid expression or levels may be any techniques known to one skilled in the art. Expression levels from the one or more biomarkers are measured in the subject and compared to the levels of the one or more biomarkers obtained from a cohort of subjects described below.

In some embodiments, MILLIPLEX® MAP multiplex assays may be used to determine the expression levels of the one or more biomarkers in a panel of biomarkers. (EMD Millipore, Billlerica, MA.) In some embodiments, Luminex-based xMAP® multiplexed immunoassays may be used to determine the expression levels of the panel of biomarkers. (Luminex Corp.; Austin, Tex.) In some embodiments, biomarker concentrations may be calculated based on 7-point standard curves using a five-parametric fit algorithm in xPONENT v4.0.3 (Luminex Corp.) Other measurement systems and techniques may also be used.

In some embodiments, a kit may be provided with reagents to measure at least one biomarker. In some embodiments, the kit may be provided with reagents to measures at least two biomarkers in a panel of biomarkers. The panel of biomarkers to be measured with the kit may include two or more biomarkers from Table I or Table II. The kit may include reagents to measure a panel of biomarkers for subjects that are treatment naïve. The kit may include reagents to measure a panel of biomarkers for subjects that have been previously treated.

Analysis of Biomarker Measurements

In some embodiments, methods for determining whether a subject has rapidly progressing lung cancer may be based upon the biomarker measurements from the subject compared to a reference cutoff level for each biomarker measured. The reference cutoff level for a plurality of biomarkers is listed in Tables I-IV.

Treatment Stratification

In some embodiments, the analysis of the biomarker panel may be used to determine a treatment regime for the subject. In some embodiments, the measurement of one or more biomarkers in the panel may be used to determine whether to begin a treatment, to continue the same treatment or to modify the treatment regime for a subject. The treatment may be modified by changing the drug administered to the subject or to add an additional drug to the existing drug treatment regime, to change the dosage or other changes. In some embodiments, other types of treatment regimes may be used such as radiation. In some embodiments, the identification of patients with rapidly progressing disease who are insensitive to standard platinum doublet-based chemotherapy may have multiple clinical implications. The identification of patients with rapidly progressing disease using the biomarker level may place the patient in a specific treatment, a different treatment or an earlier treatment in the overall treatment strategy. In some embodiments, a specific targeted chemotherapeutic agent may be selected based on the identification of rapidly progressing disease. In some embodiments, the specific chemotherapeutic agent may be changed based in the biomarker level measured relative to the cutoff value. By way of non-limiting example, VEGF-A levels in patients taking bevacizumab may be monitored and the treatment regime may be changed or not changed based on the level of VEGF-A measured and compared to the cutoff level in either Table I for treatment naïve patients or Table II for previously treated patients.

Patient Cohorts

Between 2004 and 2011, 186 patients at Rush University Medical Center (Chicago, Ill.) were enrolled and divided into the following cohorts: patients with advanced lung adenocarcinoma naïve to previous chemotherapy (n=76) and patients with advanced lung adenocarcinoma that have failed at least 1 line of chemotherapy (n=110). All stage classifications were determined according to the American Joint Committee on Cancer (AJCC) seventh edition criteria and confirmed by pathological evaluation (5, 6). All patient data was obtained after informed consent was given by the patient. The study was conducted in absolute compliance with the Institutional Review Board at Rush University Medical Center.

Measurement of Serum Biomarker Concentrations

All peripheral blood was collected pre-treatment and processed into serum using standard phlebotomy protocols. Serum was archived at −80° C. in aliquots; and no evaluable specimen were subjected to more than two freeze-thaw cycles (7-10). Serum was evaluated using the following biomarker panels: the MILLIPLEX® MAP Human Angiogenesis/Growth Factor Panel (EMD Millipore, Billerica, Mass.) and included the following assays: epidermal growth factor (EGF), angiopoietin-2, granulocyte colony-stimulating factor (G-CSF), bone morphogenic protein 9 (BMP-9), endoglin, endothelin-1, leptin, fibroblast growth factor-1 (FGF-1), FGF-2, follistatin, interleukin-8 (IL-8), hepatocyte growth factor (HGF), heparin-binding epidermal growth factor (HB-EGF), placental growth factor (PLGF), vascular endothelial growth factor-A (VEGF-A), VEGF-C and VEGF-D; the MILLIPLEX® MAP Human Soluble Cytokine Receptor Panel which includes sVEGFR1, sVEGFR2, sVEGFR3, sIL-6R, sgp130, sTNFRI, and sTNFRII; the MILLIPLEX® MAP Human Circulating Cancer Biomarker Panel 1 which includes sFasL, IL-6, prolactin, SCF, TGF-α, and TNF-α; the MILLIPLEX® MAP Human MMP1 and MMP2 panels which combine to provide MMPs -1,-2,-3,-7,-9, and -10; and the MILLIPLEX® MAP Human Cytokine/Chemokine Panel II, which includes SDF-1 (α+β). All assays were performed according to the manufacturer's recommended protocols and in a blinded fashion. All data was collected on a Luminex FlexMAP 3D system with concentrations calculated based on 7-point standard curves using a five-parametric fit algorithm in xPONENT v4.0.3 (Luminex Corp., Austin, Tex.).

Statistical Methods

One endpoint of the investigation was to evaluate associations of the circulating biomarkers tested with clinical outcome measures for patients determined to have rapidly progressing disease. Progression status values were classified as ‘slow’ or ‘rapid’ based on chosen clinically-relevant cutoff (45 days for frontline and 90 days for those second-line and above) value. Association of the slow/rapid progression state was then accomplished with low and high values of a biomarker based on cutoff values obtained from a grid search for an optimal cutoff within the potential range of the biomarker values that maximizes the p-value for disease progression via Fisher's exact test. Additionally, an adjusted p-value, which adjusts for the grid search, is also obtained. These analyses were performed regardless of regimen type. All statistical analyses were completed using the R Statistical Package.

Results

Frontline Treatment for Advanced NSCLC

A total of 27 biomarkers were identified for identifying advanced stage NSCLC patients that were chemotherapy naïve with rapidly progressing disease. The specific cutoff values, number of patients in each arm, and optimal p-values are all provided in Table I, with the distribution of these classifications for select representative biomarkers provided in FIG. 1. A complete account for all 76 biomarkers is provided in Table III. Included in the findings were biomarkers with optimal p-value 5.0.05 representing the following processes: angiogenesis (sTNFRI, sTNFRII, Follistatin, TNF-α, Betacellulin, sVEGFR3, VEGF-A, Endoglin, MMP-10, PDGF-BB, VEGF-C, IGF-I, IGFBP-3, IGFBP-5, Endothelin-1, and Amphiregulin), cancer cachexia (TNF-α, sTNFRI, sTNFRII, IGF-I, IGFBP-3, IGFBP-5, IL-6, IL-6R), and phenotypic transdifferentiation (betacellulin, IGF-I, IGFBP-3, IGFBP-5, sEGFR, and prolactin).

TABLE I Treatment Naïve Cohort Cutoff Cases Cases Value ≤ > Adjusted Biomarker (pg/mL) cutoff Cutoff p-Value p-value sTNFRI 1879.8 48 12 0.0003 0.001 sTNFRII 8493.9 49 11 0.0011 0.006 CA 19-9 20.9 62 13 0.0029 0.018 Follistatin 990.0 48 15 0.0033 0.007 Total PSA 67.1 47 28 0.0042 0.027 TNF-α 0.61 12 45 0.0050 0.029 IL-6 13.1 48 10 0.0100 0.021 TGF β1 30665.9 64 11 0.0132 0.083 Betacellulin 14.9 25 28 0.0162 0.058 CA 15.3 55.0 62 13 0.0203 0.077 sCD30 101.5 40 20 0.0219 0.111 sVEGFR3 2930.8 48 12 0.0221 0.094 VEGF-A 727.1 49 27 0.0222 0.092 Endoglin 993.8 48 15 0.0228 0.084 MMP-10 272.6 43 32 0.0244 0.122 sRAGE 84.2 39 21 0.0244 0.121 PDGF-BB 13336.6 12 40 0.0248 0.075 VEGF-C 32.9 10 53 0.0255 0.105 IGF-I 31266.8 46 30 0.0264 0.133 IGFBP-3 1452.8 53 23 0.0308 0.168 sEGFR 25073.2 29 43 0.0392 0.177 IGFBP-5 151.1 48 28 0.0428 0.192 Endothelin-1 8.0 39 24 0.0430 0.124 Amphiregulin 38.7 16 36 0.0445 0.222 GRO 3960.3 47 28 0.0453 0.181 sIL-6R 10666.7 20 40 0.0493 0.203 Prolactin 5353.7 30 28 0.0497 0.152

Previous Treatment for Advanced NSCLC

A total of 34 biomarkers were identified for identifying advanced stage NSCLC patients that failed frontline chemotherapy with rapidly progressing disease. The specific cutoff values, number of patients in each arm, and optimal p-values are all provided in Table II, with the distribution of these classifications for select representative biomarkers provided in FIG. 2. A complete account for all 76 biomarkers are provided in Table IV. Included in the findings were biomarkers with optimal p-value 50.05 representing the following processes: angiogenesis (sVEGFR2, follistatin, IGF-I, IGF-II, IGFBPs -1,-2,-3,-5,-7, IL-8, MMP-2, MMP-7, PLGF, Leptin, PDGF-AA, TNF-α, sTNFRI, sTNFRII, and VEGF-A), cancer cachexia (HGF, IGF-I, IGF-II, IGFBPs -1,-2,-3,-5,-7, IL-6, Leptin, TRAIL, sCD30, TNF-α, sTNFRI, and sTNFRII), phenotypic transdifferentiation (beta-HCG, a-fetoprotein, HE4, HGF, IGF-I, IGF-II, IGFBPs -1,-2,-3,-5,-7, osteopontin, PLGF, TGF-β, and sEGFR), and inflammation or immune response (CYFRA 21-1, GRO, IL-6, IL-8, sIL-2Ra, sIL-4R, TRAIL, sCD30, TNF-α, sTNFRI, and sTNFRII).

TABLE II Post Treatment Cohort Cutoff Cases Cases Value ≤ > Adjusted Biomarker (pg/mL) cutoff Cutoff p-Value p-value TRAIL 60.3 35 76 0.0005 0.003 sTNFRI 1506.6 62 34 0.0007 0.005 IGFBP-1 9.2 88 22 0.0009 0.005 sEGFR 50565.2 36 68 0.0012 0.007 IGF-I 7807.7 32 78 0.0021 0.008 TGF-β1 9073.7 10 77 0.0028 0.016 HGF 239.7 81 30 0.0035 0.034 MMP-7 8326.9 68 43 0.0043 0.024 MMP-2 33782.5 31 80 0.0044 0.027 α-fetoprotein 964.1 63 40 0.0050 0.031 Osteopontin 34067.2 49 54 0.0058 0.034 sVEGFR2 10846.5 13 83 0.0073 0.049 IL-6 8.0 66 37 0.0098 0.035 CYFRA 21.1 768.7 51 52 0.0119 0.03 IGF-II 507.7 37 68 0.0129 0.083 sTNFRII 7588.9 65 31 0.0151 0.093 CA 15.3 34.5 83 28 0.0156 0.056 sCD30 129.7 73 23 0.0177 0.081 sIL-4R 2096.7 14 82 0.0201 0.103 sIL-2Ralpha 836.6 55 41 0.0208 0.118 IGFBP-7 53.7 18 92 0.0211 0.137 TNF-α 91.1 93 17 0.0213 0.114 VEGF-A 1297.7 98 14 0.0227 0.05 Follistatin 968.9 11 10 0.0237 0.013 Leptin-1 11840.0 55 48 0.0239 0.154 IGFBP-3 1486.8 90 20 0.0263 0.161 PLGF 86.8 69 10 0.0268 0.127 IGFBP-5 193.5 44 66 0.0296 0.166 GRO 4254.6 80 30 0.0333 0.162 IL-8 63.9 96 15 0.0334 0.178 HE4 2993.4 88 23 0.0343 0.201 IGFBP-2 22.9 64 46 0.0344 0.169 PDGF-AA 10851.4 12 98 0.0375 0.218 B-HCG 0.16 26 85 0.0446 0.239

TABLE III Treatment Naïve Cohort Prop. ≤ No. ≤ Prop. > No. > Total Optimal Adjusted Biomarker Cutoff cutoff Cutoff cutoff Cutoff No. p-value p-value sTNFRI 1879.789 0.75 48 0.166667 12 60 0.000344 0.001 sTNFRII 8493.872 0.734694 49 0.181818 11 60 0.001094 0.006 CA.19.9 20.91498 0.758065 62 0.307692 13 75 0.00293 0.018 Follistatin 990.0075 0.625 48 1 15 63 0.003303 0.007 Total.PSA 67.1 0.808511 47 0.464286 28 75 0.004239 0.027 TNF.alpha 0.614 1 12 0.577778 45 57 0.005026 0.029 IL.6 13.09553 0.75 48 0.3 10 58 0.009969 0.021 TGF.beta1 30665.91 0.625 64 1 11 75 0.013211 0.083 Betacellulin 14.915 0.88 25 0.571429 28 53 0.016219 0.058 CA.15.3 55.03582 0.741935 62 0.384615 13 75 0.020338 0.077 sCD30 101.452 0.525 40 0.85 20 60 0.021891 0.111 sVEGFR3 2930.841 0.708333 48 0.333333 12 60 0.022146 0.094 VEGF 727.1049 0.591837 49 0.851852 27 76 0.02221 0.092 Endoglin 993.8066 0.791667 48 0.466667 15 63 0.022804 0.084 MMP.10 272.6212 0.790698 43 0.53125 32 75 0.024406 0.122 SRAGE 84.19616 0.74359 39 0.428571 21 60 0.024413 0.121 PDGF.BB 13336.56 0.416667 12 0.8 40 52 0.024825 0.075 VEGF.C 32.93148 0.4 10 0.773585 53 63 0.025532 0.105 IGF.1 31266.81 0.586957 46 0.833333 30 76 0.026375 0.133 IGFBP.3 1452.848 0.603774 53 0.869565 23 76 0.030829 0.168 sEGFR 25073.18 0.827586 29 0.581395 43 72 0.039227 0.177 IGFBP.5 151.1263 0.770833 48 0.535714 28 76 0.042771 0.192 Endothelin.1 8.012 0.615385 39 0.875 24 63 0.043036 0.124 Amphiregulin 38.666 0.5 16 0.805556 36 52 0.044476 0.222 GRO 3960.296 0.765957 47 0.535714 28 75 0.045349 0.181 sIL.6R 10666.72 0.45 20 0.725 40 60 0.049324 0.203 Prolactin 5353.744 0.8 30 0.535714 28 58 0.049721 0.152 sIL.2Ralpha 1390.342 0.702128 47 0.384615 13 60 0.051904 0.177 FGF.2.1 77.31547 0.744186 43 0.466667 15 58 0.061694 0.192 VEGF.D 102.6913 0.782609 46 0.529412 17 63 0.063445 0.242 PDGF.AA 47558.59 0.607843 51 0.833333 24 75 0.065221 0.315 Tenascin.C 1130.558 0.84 25 0.592593 27 52 0.068469 0.207 RANTES 38837 0.821429 28 0.595745 47 75 0.071934 0.311 IGFBP.4 19.09024 0.821429 28 0.604167 48 76 0.072878 0.299 sIL.1RI 61.50253 0.704545 44 0.4375 16 60 0.073962 0.299 MIF 192.023 0.875 16 0.627119 59 75 0.074126 0.225 IGF.II 304.3222 0.5 18 0.745455 55 73 0.078394 0.348 Angiopoietin.2 2037.563 0.8 40 0.565217 23 63 0.080545 0.237 VEGF.A 383.2805 0.785714 42 0.571429 21 63 0.086763 0.279 MMP.7 8619.594 0.595238 42 0.787879 33 75 0.087197 0.313 CYFRA.21.1 1129.805 0.78125 32 0.538462 26 58 0.09016 0.356 C.Peptide 3468.038 0.640625 64 0.909091 11 75 0.093476 0.27 CEA 24090.46 0.725806 62 0.461538 13 75 0.099668 0.212 sIL.1RII 7636.054 0.6875 48 0.416667 12 60 0.102237 0.289 sVEGFR1 112.065 0.416667 12 0.6875 48 60 0.102237 0.421 HGF 594.8866 0.74026 77 0.5 14 91 0.108812 0.452 OPN 41149.3 0.738095 42 0.5 16 58 0.118998 0.34 FP 1001.211 0.738095 42 0.5 16 58 0.118998 0.451 IGFBP.7 88.26316 0.725806 62 0.5 14 76 0.119713 0.423 MMP.2 29712.45 0.555556 27 0.75 48 75 0.121477 0.46 Beta.HCG 0.227662 0.866667 15 0.633333 60 75 0.122652 0.372 EGF.1 214.3822 0.590909 22 0.806452 31 53 0.123603 0.319 HB.EGF 566.2644 0.746835 79 0.5 10 89 0.135974 0.491 CA125 27.12589 0.74359 39 0.526316 19 58 0.137596 0.297 EGF 7.196994 0.578947 19 0.772727 44 63 0.138297 0.407 Leptin.1 24785.75 0.625 48 0.9 10 58 0.142086 0.412 sgp130 247102.2 0.68 50 0.4 10 60 0.149211 0.483 SCF 44.52682 0.833333 18 0.631579 57 75 0.15047 0.568 BMP.9 175.4831 0.641026 39 0.833333 24 63 0.151565 0.529 Leptin 25030.11 0.666667 51 0.916667 12 63 0.153203 0.471 MMP.9 60960.72 0.9 10 0.646154 65 75 0.153985 0.527 MMP.1 1716.111 0.454545 11 0.71875 64 75 0.158025 0.535 TNF.Alpha 60.611 0.71875 64 0.454545 11 75 0.158025 0.471 G.CSF 29.014 0.76 50 0.538462 13 63 0.167521 0.572 TRAIL 43.58466 0.809524 21 0.62963 54 75 0.173113 0.63 IGFBP.2 4.596466 0.5 12 0.71875 64 76 0.178147 0.6 sIL.4R 2621.552 0.724138 29 0.548387 31 60 0.188122 0.579 IGFBP.6 93.69005 0.576923 26 0.74 50 76 0.194708 0.64 PDGF.AB.BB 53802.61 0.756757 37 0.605263 38 75 0.2169 0.759 Epiregulin 27.317 0.793103 29 0.625 24 53 0.226799 0.655 sVEGFR2 15112.92 0.682927 41 0.526316 19 60 0.263893 0.675 FGF.1 22.43221 0.74359 78 0.545455 11 89 0.27982 0.558 HE4 2394.042 0.738095 42 0.606061 33 75 0.318789 0.847 IGFBP.1 4.214632 0.742857 35 0.634146 41 76 0.33443 0.836 TGF.alpha 25.20851 0.655172 58 0.777778 18 76 0.396001 0.889 IL.8 8.220301 0.6 20 0.714286 56 76 0.405188 0.812 FGF.2 43.838 0.735849 53 0.6 10 63 0.452219 0.778 PLGF 19.93105 0.75 56 0.666667 33 89 0.466906 0.959

TABLE IV Post Treatment Cohort Prop. ≤ No. ≤ Prop. > No > Total Optimal Adjusted Biomarker Cutoff cutoff Cutoff Cutoff Cutoff No. p-value p-value TRAIL 60.26394 0.485714 35 0.828947 76 111 0.00046 0.003 sTNFRI 1506.637 0.83871 62 0.5 34 96 0.000738 0.005 IGFBP.1 9.170897 0.795455 88 0.409091 22 110 0.000918 0.005 sEGFR 50565.21 0.5 36 0.823529 68 104 0.001168 0.007 IGF.1 7807.722 0.5 32 0.807692 78 110 0.002077 0.008 TGF.beta1 9073.747 0.3 10 0.792208 77 87 0.002814 0.016 HGF 239.72 0.802469 81 0.5 30 111 0.003543 0.034 MMP.7 8326.919 0.823529 68 0.55814 43 111 0.004292 0.024 MMP.2 33782.48 0.516129 31 0.8 80 111 0.004435 0.027 FP 964.1461 0.650794 63 0.9 40 103 0.005027 0.031 OPN 34067.17 0.877551 49 0.62963 54 103 0.005844 0.034 sVEGFR2 10846.5 0.384615 13 0.771084 83 96 0.007303 0.049 IL.6 8.036509 0.833333 66 0.594595 37 103 0.009841 0.035 CYFRA.21.1 768.7162 0.862745 51 0.634615 52 103 0.01185 0.03 IGF.II 507.7223 0.540541 37 0.794118 68 105 0.01287 0.083 sTNFRII 7588.941 0.8 65 0.548387 31 96 0.015052 0.093 CA.15.3 34.52987 0.783133 83 0.535714 28 111 0.015644 0.056 sCD30 129.7465 0.657534 73 0.913043 23 96 0.017669 0.081 sIL.4R 2096.678 0.428571 14 0.768293 82 96 0.020119 0.103 sIL.2Ralpha 836.577 0.818182 55 0.585366 41 96 0.020777 0.118 IGFBP.7 53.66313 0.944444 18 0.673913 92 110 0.021058 0.137 TNF.Alpha 91.0655 0.666667 93 0.941176 17 110 0.021294 0.114 VEGF 1297.73 0.755102 98 0.428571 14 112 0.022711 0.05 Follistatin 968.871 0.363636 11 0.9 10 21 0.023736 0.013 Leptin.1 11840.03 0.654545 55 0.854167 48 103 0.023887 0.154 IGFBP.3 1486.814 0.766667 90 0.5 20 110 0.026339 0.161 PLGF 86.76473 0.710145 69 0.3 10 79 0.026822 0.127 IGFBP.5 193.509 0.840909 44 0.636364 66 110 0.029601 0.166 GRO 4254.61 0.65 80 0.866667 30 110 0.033305 0.162 IL.8 63.90276 0.75 96 0.466667 15 111 0.033355 0.178 HE4 2993.413 0.772727 88 0.521739 23 111 0.034259 0.201 IGFBP.2 22.89657 0.796875 64 0.608696 46 110 0.034418 0.169 PDGF.AA 10851.41 0.416667 12 0.744898 98 110 0.03748 0.218 Beta.HCG 0.1585 0.884615 26 0.670588 85 111 0.044598 0.239 RANTES 112016.4 0.677083 96 0.928571 14 110 0.062467 0.353 Amphiregulin 18.45182 0.818182 22 0.588235 51 73 0.066034 0.236 FGF.2.1 66.75736 0.677966 59 0.840909 44 103 0.069766 0.338 sgp130 242843.7 0.771429 70 0.576923 26 96 0.075576 0.348 C.Peptide 3348.411 0.734694 98 0.454545 11 109 0.078178 0.289 CA.19.9 9.618456 0.615385 39 0.777778 72 111 0.079573 0.34 Endoglin 662.959 0.4 10 0.818182 11 21 0.080495 0.042 Total.PSA 528.9822 0.764706 85 0.576923 26 111 0.080808 0.25 Tenascin.C 3835.28 0.698413 63 0.4 10 73 0.081497 0.309 PDGF.AB.BB 66331.14 0.673913 92 0.888889 18 110 0.089174 0.436 sVEGFR1 360.3484 0.763158 76 0.55 20 96 0.091301 0.412 TGF.alpha 9.548921 0.783333 60 0.634615 52 112 0.096262 0.408 HB.EGF 311.0071 0.542857 35 0.733333 45 80 0.099623 0.442 sVEGFR3 2158.519 0.66129 62 0.823529 34 96 0.102868 0.428 IGFBP.4 79.29117 0.752809 89 0.571429 21 110 0.111044 0.546 MMP.1 6532.742 0.766234 77 0.617647 34 111 0.115508 0.467 sIL.1RI 65.47095 0.753086 81 0.533333 15 96 0.116428 0.506 CA125 56.64133 0.777778 81 0.636364 22 103 0.180181 0.453 MIF 303.2853 0.671875 64 0.787234 47 111 0.204763 0.685 MMP.9 55018.82 0.833333 24 0.689655 87 111 0.204937 0.752 FGF.1 10.48625 0.6 50 0.758621 29 79 0.218824 0.596 SCF 77.97 0.741935 93 0.611111 18 111 0.264052 0.827 sIL.1RII 9586.476 0.697674 86 0.9 10 96 0.273533 0.817 PDGF.BB 28385.66 0.68254 63 0.5 10 73 0.295399 0.791 Betacellulin 17.683 0.717949 39 0.588235 34 73 0.323903 0.877 Prolactin 15306.05 0.764045 89 0.642857 14 103 0.335635 0.848 EGF.1 31.14602 0.785714 14 0.627119 59 73 0.354469 0.865 sIL.6R 16360.28 0.683333 60 0.777778 36 96 0.357432 0.938 MMP.10 290.6754 0.75 72 0.666667 39 111 0.380985 0.877 G.CSF 4.271 0.727273 11 0.5 10 21 0.386997 0.252 BMP.9 167.918 0.5 10 0.727273 11 21 0.386997 0.228 Leptin 10637.02 0.5 10 0.727273 11 21 0.386997 0.236 VEGF.C 75.486 0.5 10 0.727273 11 21 0.386997 0.225 VEGF.A 228.021 0.727273 11 0.5 10 21 0.386997 0.232 CEA 24257.18 0.702128 94 0.823529 17 111 0.388648 0.739 TNF.alpha 2.975929 0.666667 21 0.768293 82 103 0.400402 0.966 SRAGE 54.18307 0.8 20 0.697368 76 96 0.417848 0.922 Epiregulin 71.12777 0.678571 56 0.588235 17 73 0.564203 0.636 IGFBP.6 150.9202 0.704545 88 0.772727 22 110 0.605457 1.00 EGF 5.662 0.7 10 0.545455 11 21 0.659443 0.542 Angiopoietin.2 1761.646 0.545455 11 0.7 10 21 0.659443 0.524 Endothelin.1 3.598 0.7 10 0.545455 11 21 0.659443 0.392 VEGF.D 49.626 0.545455 11 0.7 10 21 0.659443 0.571

The practice of the present invention will employ, unless otherwise indicated, conventional methods for measuring the level of the biomarker within the skill of the art. The techniques may include, but are not limited to, molecular biology and immunology. Such techniques are explained fully in the literature. See, e.g., Sambrook, et al. Molecular Cloning: A Laboratory Manual (Current Edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.); Current Protocols in Molecular Biology (Eds. A Ausubel et al., NY: John Wiley & Sons, Current Edition); DNA Cloning: A Practical Approach, vol. I & II (D. Glover, ed.); Oligonucleotide Synthesis (N. Gait, ed., Current Edition); Nucleic Acid Hybridization (B. Hames & S. Higgins, eds., Current Edition); Transcription and Translation (B. Hames & S. Higgins, eds., Current Edition).

The above Figures and disclosure are intended to be illustrative and not exhaustive. This description will suggest many variations and alternatives to one of ordinary skill in the art. All such variations and alternatives are intended to be encompassed within the scope of the attached claims. Those familiar with the art may recognize other equivalents to the specific embodiments described herein which equivalents are also intended to be encompassed by the attached claims.

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Claims

1. A method for measuring a panel of biomarkers in a subject suspected of having rapidly progressing lung cancer the method comprising:

obtaining a biological sample from the subject;
determining whether the subject is treatment naïve or has received at least one treatment;
assaying a level of each biomarker in a first biomarker panel or a second biomarker panel in the biological sample, the first biomarker panel comprising sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6 for treatment naïve subjects and the second biomarker panel comprising TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6 for subjects having received at least one treatment.

2. The method according to claim 1, comprising determining the level of each biomarker for the first panel of biomarkers wherein the first biomarker-panel consists of sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6.

3. The method according to claim 1, comprising determining the level of each biomarker for the second panel of biomarkers wherein the second biomarker panel consists of TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL 6.

4. The method according to claim 1, wherein the lung cancer is non-small cell lung cancer.

5. The method according to claim 1, wherein the biological sample comprises plasma sample or serum sample.

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Patent History
Patent number: 10365281
Type: Grant
Filed: Dec 8, 2014
Date of Patent: Jul 30, 2019
Patent Publication Number: 20160313336
Assignee: Rush University Medical Center (Chicago, IL)
Inventors: Jeffrey A. Borgia (Chicago, IL), Sanjib Basu (Chicago, IL)
Primary Examiner: Sean E Aeder
Application Number: 15/102,647
Classifications
Current U.S. Class: Non/e
International Classification: G01N 33/53 (20060101); G01N 33/574 (20060101);